






import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__				     =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__				     =			[
    ['''attention''', '''attn'''],
    ['''encoder_attention''', '''encoder_attn'''],
    ['''q_lin''', '''q_proj'''],
    ['''k_lin''', '''k_proj'''],
    ['''v_lin''', '''v_proj'''],
    ['''out_lin''', '''out_proj'''],
    ['''norm_embeddings''', '''layernorm_embedding'''],
    ['''position_embeddings''', '''embed_positions'''],
    ['''embeddings''', '''embed_tokens'''],
    ['''ffn.lin''', '''fc'''],
]

def        UpperCAmelCase__		(						lowerCamelCase_       :       Union[str, Any]				):
			if k == "embeddings.weight":
						return "shared.weight"

			for parlai_name, hf_name in PATTERNS:
						__a						:  List[str]									=     k.replace(lowerCamelCase_   ,  lowerCamelCase_				)

			if k.startswith('encoder'				):
						__a						:  str									=     k.replace('.attn'   ,  '.self_attn'				)
						__a						:  Tuple									=     k.replace('norm1'   ,  'self_attn_layer_norm'				)
						__a						:  Any									=     k.replace('norm2'   ,  'final_layer_norm'				)
			elif k.startswith('decoder'				):
						__a						:  int									=     k.replace('norm1'   ,  'self_attn_layer_norm'				)
						__a						:  Tuple									=     k.replace('norm2'   ,  'encoder_attn_layer_norm'				)
						__a						:  Optional[Any]									=     k.replace('norm3'   ,  'final_layer_norm'				)
			return k

def        UpperCAmelCase__		(						lowerCamelCase_       :       List[Any]				):
			__a						:  str									=     [
			    'model.encoder.layernorm_embedding.weight',
			    'model.encoder.layernorm_embedding.bias',
			    'model.decoder.layernorm_embedding.weight',
			    'model.decoder.layernorm_embedding.bias',
			]
			for k in keys:
						__a						:  Optional[int]									=     sd.pop(lowerCamelCase_				)
						__a						:  List[Any]									=     k.replace('layernorm_embedding'   ,  'layer_norm'				)
						assert new_k not in sd
						__a						:  Tuple									=     v


SCREAMING_SNAKE_CASE__				     =			['''START''']

@torch.no_grad()
def        UpperCAmelCase__		(						lowerCamelCase_       :       List[Any]   ,  lowerCamelCase_       :       List[Any]   ,  lowerCamelCase_       :       List[Any]				):
			__a						:  str									=     torch.load(lowerCamelCase_   ,  map_location='cpu'				)
			__a						:  Any									=     model['model']
			__a						:  Optional[int]									=     BlenderbotConfig.from_json_file(lowerCamelCase_				)
			__a						:  Any									=     BlenderbotForConditionalGeneration(lowerCamelCase_				)
			__a						:  Dict									=     m.model.state_dict().keys()
			__a						:  Tuple									=     []
			__a						:  int									=     {}
			for k, v in sd.items():
						if k in IGNORE_KEYS:
									continue

						__a						:  Union[str, Any]									=     rename_state_dict_key(lowerCamelCase_				)
						if new_k not in valid_keys:
									failures.append([k, new_k]				)
						else:
									__a						:  str									=     v
			if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
						rename_layernorm_keys(lowerCamelCase_				)
			m.model.load_state_dict(lowerCamelCase_   ,  strict=lowerCamelCase_				)
			m.half()
			m.save_pretrained(lowerCamelCase_				)


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__				     =			argparse.ArgumentParser()
							# Required parameters
							parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
							parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
							parser.add_argument(
							    '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
							)
							SCREAMING_SNAKE_CASE__				     =			parser.parse_args()
							convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)







"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


SCREAMING_SNAKE_CASE			  =  logging.get_logger(__name__)



class 	__a	(   _lowerCAmelCase						):






       def __init__(	self			:				str							,   *UpperCAmelCase_			:				str							,   **UpperCAmelCase_			:				Any      )->	None:







              """simple docstring"""
              warnings.warn(
                  "The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
                  " use YolosImageProcessor instead."							,   UpperCAmelCase_							,   )
              super().__init__(*UpperCAmelCase_							,   **UpperCAmelCase_      )





